publication . Part of book or chapter of book . 2011

NcPred for accurate nuclear protein prediction using n-mer statistics with various classification algorithms

Md. Saiful Islam; Alaol Kabir; Kazi Sakib; Md. Alamgir Hossain;
Open Access English
  • Published: 01 Jan 2011
  • Publisher: Springer
  • Country: United Kingdom
Abstract
Prediction of nuclear proteins is one of the major challenges in genome annotation. A method, NcPred is described, for predicting nuclear proteins with higher accuracy exploiting n − mer statistics with different classification algorithms namely Alternating Decision (AD) Tree, Best First (BF) Tree, Random Tree and Adaptive (Ada) Boost. On BaCello dataset [1], NcPred improves about 20% accuracy with Random Tree and about 10% sensitivity with Ada Boost for Animal proteins compared to existing techniques. It also increases the accuracy of Fungal protein prediction by 20% and recall by 4% with AD Tree. In case of Human protein, the accuracy is improved by about 25% ...
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: G400, G700, AdaBoost, Statistics, Statistical classification, Animal proteins, Genome project, Computer science, Nuclear protein, Fungal protein, Random tree
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